Variable and model approaches are becoming increasing important due to the advances in DNA chip technology, resulting in as many as 500,000 single-nucleotide polymorphisms (SNPs). However, it is unclear when a particular statistical method should be used, and how Bayesian methods compare with more standard frequentist approaches. The goal of this paper is to compare different methods using Bayesian and non-Bayesian approaches for binary and continuous outcomes. We used three replication sets from the Genetic Analysis Workshop (GAW15) simulated data and focused on chromosomes 6 and 18 for rheumatoid arthritis (RA) affection status, and on chromosome 11 for the continuous outcome, IgM. In addition to chromosomes 6, 11, and 18, chromosome 19 was used as a control (null).
The data were simulated to have a signal at 115.28 cM (SNPs 389–394) on chromosome 11 with the continuous outcome, IgM, at 49.46 cM (SNPs 152–154) on chromosome 6 with RA affection status, and at 94.27 cM (SNPs 267–270) with a controlled effect of DR on anti-CCp and an increased risk on RA on chromosome 18.
For the continuous phenotype, we used the data from affected sib pairs, since IgM was observed only on the cases. Thus, we divided the data in two data sets, training and testing. The training set consisted of one of the affected siblings from each pedigree chosen at random, and the testing set consisted of the remaining affected siblings. For the binary analysis, the RA case was one of the affected siblings, from the 1500 simulated affected sib pairs, and the RA case was selected such that the percentages of males and females between the RA siblings and the unrelated controls (2000 controls overall) were similar (i.e., frequency matched on sex). Furthermore, covariates of age, sex, and smoking status were also available for all individuals. Overall, 1500 RA cases and 2000 controls were used to analyze 674, 303, 93 SNPs on chromosome 6, 18, and 19, respectively. One thousand five hundred affected pairs were selected for the IgM analysis using 492 SNPs on chromosome 11.